Automatic YouTube Audio-to-Text Transcription

Unleash the power of automation with this user-friendly tool that downloads audio from YouTube videos, transcribes the content into text, detects the language of the transcribed text, and saves the transcription to a text file.

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With the increasing popularity of online video content, there’s a growing need for transcription services. Transcribing audio from YouTube videos is a common task for content creators, researchers, and educators. It can be useful for generating subtitles, creating transcripts for accessibility, or analyzing spoken content. However, manual transcription is a laborious and time-consuming process, especially when dealing with lengthy or numerous videos. Professional transcription services can be expensive, and automated transcription tools may have limitations in terms of accuracy and language support. This is where this Python automation tool comes into play, offering an open-source, customizable solution for YouTube audio transcription that supports a wide range of languages and can be easily adapted to specific needs.

I’ve developed a Python automation tool that simplifies the process of downloading audio from YouTube videos, transcribing it, and saving the transcription to a text file. This tool is designed to facilitate the transcription of YouTube videos into text format. It eliminates the need for time-consuming manual transcription by automating the process through a series of well-defined steps. The user-friendly interface allows users to input a YouTube video URL, which is then processed to extract the audio, convert it into text, and save the transcription as a text file. This efficient and convenient solution is ideal for those who require quick and accurate transcriptions for various purposes, such as research, content creation, or accessibility.

In this blog post, I’ll provide an overview of the tool, discuss its benefits, and walk through some working examples to demonstrate its functionality.

Key Features

  • User-friendly: Designed for ease of use, the script prompts users to enter a YouTube video URL, minimizing the need for complicated setup processes.
  • Efficient Audio Extraction: The tool utilizes the pytube library to effectively filter and download the audio stream from the specified YouTube video.
  • High-Quality Transcription: The whisper library, a powerful speech-to-text tool, is employed to accurately transcribe the downloaded audio into text.
  • Convenient Output: The transcription is saved as a text file in the same directory as the script, ensuring easy access and sharing capabilities.


There are several benefits of using this Python automation tool for YouTube audio transcription:

  1. Time-saving: The script automates the entire transcription process, allowing you to save time and focus on other tasks.
  2. Cost-effective: The script is free to use and doesn’t require any paid subscriptions or services.
  3. Language detection: The script detects the language of the transcribed text, making it easy to handle multilingual content.
  4. Customizable: The script’s code is open-source and can be easily modified to fit your specific needs.

The Automation Tool

The Python tool leverages the pytube, whisper, and langdetect libraries to download the audio from YouTube videos, transcribe the audio to text, and detect the language of the transcribed text. The complete script can be found here, along with detailed instructions for setting up and using the script.

Before using the script, you need to install the required libraries. You can do this by running the following command:

pip install pytube whisper langdetect

If you have any error with using whisper package, try installing it from GitHub as follows:

pip install git+

Here’s an overview of the script’s workflow:

  1. The script asks the user for a YouTube video URL.
  2. It uses pytube to download the audio stream from the specified video.
  3. The downloaded audio is transcribed to text using the whisper library.
  4. The transcribed text’s language is detected using the langdetect library.
  5. Finally, the transcribed text is saved to a text file with a language-specific suffix.

To use the script, simply run it in your terminal or command prompt, and provide the YouTube video URL when prompted:



To demonstrate the script’s functionality, let’s walk through a working example.

Example: Downloading and transcribing a YouTube video

Suppose you have a YouTube video URL: that you’d like to transcribe. Follow these steps:

  1. Clone the GitHub repository containing the script.

  2. Install the required libraries by running pip install pytube whisper langdetect in your terminal or command prompt.

  3. Run the script in your terminal or command prompt:

  4. When prompted, enter the YouTube video URL.

    Enter the YouTube video URL:
  5. The script will download the audio, transcribe it, detect the language, and save the transcription to a text file with a language-specific suffix.

  6. The script will download the audio, transcribe it, detect the language, and save the transcription to a text file, such as output_en.txt for English.

In this blog post, I’ve introduced a Python automation tool for downloading, transcribing, and saving the transcription of YouTube video audios. The tool offers a convenient and efficient way to handle YouTube audio transcription tasks and can be easily customized to suit your requirements. By automating this process, you can save time, reduce costs, and improve your workflow when dealing with YouTube audio transcription tasks. Whether you’re a content creator, researcher, or simply someone who needs transcriptions of YouTube videos, this script is a valuable tool to have in your arsenal.

Don’t hesitate to check out the tool on GitHub and give it a try. If you have any questions, suggestions, or want to contribute, feel free to open an issue or submit a pull request. Happy transcribing!

🎓🌟 Feel free to contribute, share, and spread the love 💖💬🌍

Javed Ali
Doctoral Researcher

My research involves multi-hazards risk assessment and analyzing compound climate and weather extreme events to better understand their interrelationships at different spatial and temporal scales as well as assessing their corresponding socio-economic impacts using machine learning and statistical methods.

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